WO2025108337A1 - Image reproduction method and device - Google Patents

Abstract

An image reproduction method and device, and a storage medium. The method comprises: obtaining first metadata information corresponding to a first image (S101), wherein the first image is composed of more than one single-channel images, and the first metadata information comprises parameter information respectively corresponding to the more than one single-channel images and image processing information corresponding to the first image; for a sample to be observed, on the basis of the parameter information respectively corresponding to the more than one single-channel images, acquiring the more than one single-channel images corresponding to said sample (S102); superimposing the more than one single-channel images corresponding to said sample to generate a second image corresponding to said sample (S103); and on the basis of the image processing information, performing image processing on the second image (S104). Therefore, a corresponding superimposed image can be automatically obtained for a sample to be observed on the basis of a current superimposed image.

Description

Image reproduction method and device

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure is based on the Chinese patent application with application number 202311561773.2 and application date November 21, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this disclosure as a reference.

Technical Field

The present disclosure relates to image processing technology, and in particular to an image reproduction method and device.

Background Art

With the development of image processing and analysis technology, superimposed images composed of multiple single-channel images have become mainstream. For example, when observing a sample, multiple single-channel images of the sample are usually obtained, and the multiple single-channel images are superimposed to obtain a superimposed image so that the operator can observe the sample from the superimposed image.

Summary of the invention

A first aspect embodiment of the present disclosure proposes an image reproduction method, which includes: obtaining first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information includes parameter information corresponding to the more than one single-channel images and image processing information corresponding to the first image; for a sample to be observed, based on the parameter information corresponding to the more than one single-channel images, respectively obtaining more than one single-channel images corresponding to the sample to be observed; superimposing the more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed; and performing image processing based on the second image and the image processing information corresponding to the first image.

Optionally, wherein the first metadata information also includes image processing information respectively corresponding to one or more single-channel images constituting the first image, before synthesizing more than one single-channel images corresponding to the sample to be observed, the method also includes: performing image processing on the one or more single-channel images corresponding to the sample to be observed, based on the image processing information respectively corresponding to the one or more single-channel images constituting the first image.

Optionally, the image processing includes artificial intelligence (AI) analysis, the image processing information includes an AI analysis target and information about an AI analysis tool, and performing image processing based on the image processing information includes: acquiring the AI analysis tool according to the information about the AI analysis tool; and using the AI analysis tool to perform image processing to achieve the AI analysis target.

Optionally, the AI analysis tool includes one or more AI analysis models; the information about the AI analysis tool includes at least one of the following: used to obtain relevant information for each AI analysis model; each AI analysis model.

Optionally, the information about the AI analysis tool also includes feature information of each AI analysis model.

Optionally, the AI analysis tool includes multiple AI analysis models; after obtaining the AI analysis tool based on the information about the AI analysis tool, the method further includes: determining a target AI analysis model from the multiple AI analysis models; using the AI analysis tool to perform image processing to achieve the AI analysis goal includes: using the target AI analysis model to perform image processing to achieve the AI analysis goal.

Optionally, determining the target AI analysis model from the multiple AI analysis models includes: receiving an AI analysis requirement input by a user; and determining the target AI analysis model based on the AI analysis requirement and feature information of each AI analysis model.

Optionally, determining a target AI analysis model from the multiple AI analysis models includes: displaying information about the multiple AI analysis models; and determining a target AI analysis model from the multiple AI analysis models based on a user's selection input.

Optionally, the AI analysis objectives include at least one of the following: determining cell confluence; determining cell count; or determining cell transfection rate.

Optionally, before obtaining more than one single-channel images corresponding to the sample to be observed based on parameter information respectively corresponding to the more than one single-channel images, it also includes: outputting a first prompt information, wherein the first prompt information is used to prompt a user to confirm whether to generate the second image for the sample to be observed; and receiving a first instruction, wherein the first instruction instructs the user to confirm generating the second image for the sample to be observed.

Optionally, for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtaining more than one single-channel images corresponding to the sample to be observed includes: for each single-channel image constituting the first image, based on the parameter information corresponding to the single-channel image, adjusting the parameter setting of the image generating device; and using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed.

Optionally, before adjusting the parameter settings of the image generating device, the method also includes: determining whether the parameter settings of the image generating device can be adjusted based on parameter information corresponding to the single-channel image; and adjusting the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image includes: when the parameter settings of the image generating device can be adjusted, adjusting the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image.

Optionally, the method further comprises: when the parameter setting of the image generating device cannot be adjusted, outputting second prompt information, wherein the second prompt information is used to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Optionally, the second prompt information further includes guidance information for guiding the user to manually adjust the parameter that cannot be automatically adjusted.

Optionally, the image generating device includes a microscope and a camera, and the parameter information includes at least one of an imaging mode, a microscope parameter, and a shooting parameter; determining whether the parameter setting of the image generating device can be adjusted includes at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

Optionally, before using the adjusted image generating device to acquire a single-channel image corresponding to the sample to be observed, the method further includes: receiving a second instruction, wherein the second instruction indicates that parameter settings of the image generating device have been successfully adjusted.

Optionally, before using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed, the method also includes: determining whether the parameter setting of the image generating device is effective; using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed includes: after the parameter setting of the image generating device is effective, using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed.

Optionally, the method further includes: storing parameter information respectively corresponding to the more than one single-channel images and image processing information corresponding to the second image as second metadata information corresponding to the second image.

Optionally, the method also includes: storing parameter information corresponding to the more than one single-channel images, image processing information corresponding to the second image, and image processing information corresponding to one or more single-channel images constituting the second image as second metadata information corresponding to the second image.

A second aspect embodiment of the present disclosure proposes an image reproduction method, the method comprising: obtaining first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information comprises parameter information respectively corresponding to the more than one single-channel images and image processing information respectively corresponding to one or more single-channel images constituting the first image; for a sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtaining more than one single-channel images corresponding to the sample to be observed; for one or more single-channel images corresponding to the sample to be observed, based on the image processing information respectively corresponding to the one or more single-channel images constituting the first image, performing image processing; and superimposing the more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

Optionally, the image processing includes artificial intelligence (AI) analysis, the image processing information includes an AI analysis target and information about an AI analysis tool, and performing image processing based on the image processing information includes: acquiring the AI analysis tool according to the information about the AI analysis tool; and using the AI analysis tool to perform image processing to achieve the AI analysis target.

Optionally, the AI analysis tool includes one or more AI analysis models; the information about the AI analysis tool includes at least one of the following: used to obtain relevant information for each AI analysis model; each AI analysis model.

Optionally, the information about the AI analysis tool also includes feature information of each AI analysis model.

Optionally, the AI analysis tool includes multiple AI analysis models; after obtaining the AI analysis tool based on the information about the AI analysis tool, the method further includes: determining a target AI analysis model from the multiple AI analysis models; using the AI analysis tool to perform image processing to achieve the AI analysis goal includes: using the target AI analysis model to perform image processing to achieve the AI analysis goal.

Optionally, determining a target AI analysis model from the multiple AI analysis models comprises: receiving an AI analysis requirement input by a user; and determining the target AI analysis model according to the AI analysis requirement and feature information of each AI analysis model. Standard AI analysis model.

Optionally, determining a target AI analysis model from the multiple AI analysis models includes: displaying information about the multiple AI analysis models; and determining a target AI analysis model from the multiple AI analysis models based on a user's selection input.

Optionally, the AI analysis objectives include at least one of the following: determining cell confluence; determining cell count; or determining cell transfection rate.

Optionally, before obtaining more than one single-channel images corresponding to the sample to be observed based on parameter information respectively corresponding to the more than one single-channel images, it also includes: outputting a first prompt information, wherein the first prompt information is used to prompt a user to confirm whether to generate the second image for the sample to be observed; and receiving a first instruction, wherein the first instruction instructs the user to confirm generating the second image for the sample to be observed.

Optionally, for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtaining more than one single-channel images corresponding to the sample to be observed includes: for each single-channel image constituting the first image, based on the parameter information corresponding to the single-channel image, adjusting the parameter setting of the image generating device; and using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed.

Optionally, before adjusting the parameter settings of the image generating device, the method also includes: determining whether the parameter settings of the image generating device can be adjusted based on parameter information corresponding to the single-channel image; and adjusting the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image includes: when the parameter settings of the image generating device can be adjusted, adjusting the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image.

Optionally, the method further comprises: when the parameter setting of the image generating device cannot be adjusted, outputting second prompt information, wherein the second prompt information is used to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Optionally, the second prompt information further includes guidance information for guiding the user to manually adjust the parameter that cannot be automatically adjusted.

Optionally, the image generating device includes a microscope and a camera, and the parameter information includes at least one of an imaging mode, a microscope parameter, and a shooting parameter; determining whether the parameter setting of the image generating device can be adjusted includes at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

Optionally, before using the adjusted image generating device to acquire a single-channel image corresponding to the sample to be observed, the method further includes: receiving a second instruction, wherein the second instruction indicates that parameter settings of the image generating device have been successfully adjusted.

Optionally, before using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed, the method also includes: determining whether the parameter setting of the image generating device is effective; using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed includes: after the parameter setting of the image generating device is effective, using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed.

Optionally, the method also includes: storing parameter information corresponding to the more than one single-channel images, image processing information corresponding to the second image, and image processing information corresponding to one or more single-channel images constituting the second image as second metadata information corresponding to the second image.

A third aspect embodiment of the present disclosure provides an image reproduction device, the device comprising: a first acquisition module, used to obtain first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information comprises parameter information respectively corresponding to the more than one single-channel images and image processing information corresponding to the first image; a second acquisition module, used to acquire, for a sample to be observed, more than one single-channel images corresponding to the sample to be observed based on the parameter information respectively corresponding to the more than one single-channel images; a generation module, used to superimpose the more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed; and a processing module, used to perform image processing on the second image based on the image processing information corresponding to the first image.

Optionally, the first metadata information also includes image processing information corresponding to one or more single-channel images constituting the first image, and the processing module is further used to: perform image processing on one or more single-channel images corresponding to the sample to be observed, based on the image processing information corresponding to one or more single-channel images constituting the first image.

Optionally, the image processing includes artificial intelligence (AI) analysis, the image processing information includes AI analysis targets and information about the AI analysis tool, and the processing module is used to: obtain the AI analysis tool based on the information about the AI analysis tool; and use the AI analysis tool to perform image processing to achieve the AI analysis target.

Optionally, the AI analysis tool includes one or more AI analysis models; the information about the AI analysis tool includes at least one of the following: used to obtain relevant information for each AI analysis model; each AI analysis model.

Optionally, the information about the AI analysis tool also includes feature information of each AI analysis model.

Optionally, the AI analysis tool includes multiple AI analysis models; the device also includes: a determination module, used to determine a target AI analysis model from the multiple AI analysis models; the processing module is used to: use the target AI analysis model to perform image processing to achieve the AI analysis goal.

Optionally, the determination module is used to: receive AI analysis requirements input by a user; and determine the target AI analysis model based on the AI analysis requirements and feature information of each AI analysis model.

Optionally, the determination module is used to: display information about the multiple AI analysis models; and determine a target AI analysis model from the multiple AI analysis models based on a user's selection input.

Optionally, the AI analysis objectives include at least one of the following: determining cell confluence; determining cell count; or determining cell transfection rate.

Optionally, the device also includes: a transceiver module: used to output a first prompt message, wherein the first prompt message is used to prompt a user to confirm whether to generate the second image for the sample to be observed; and used to receive a first instruction, wherein the first instruction instructs the user to confirm the generation of the second image for the sample to be observed.

Optionally, the second acquisition module includes: an adjustment unit for adjusting parameter settings of an image generating device for each single-channel image constituting the first image based on parameter information corresponding to the single-channel image; and an acquisition unit for using the adjusted image generating device to acquire the single-channel image corresponding to the sample to be observed.

Optionally, the second acquisition module also includes: a first determination unit, used to determine whether the parameter settings of the image generating device can be adjusted based on the parameter information corresponding to the single-channel image; and the adjustment unit, used to adjust the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image when the parameter settings of the image generating device can be adjusted.

Optionally, the second acquisition module further includes: an output unit, configured to output second prompt information when parameter settings of the image generating device cannot be adjusted, wherein the second prompt information is configured to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Optionally, the second prompt information further includes guidance information for guiding the user to manually adjust the parameter that cannot be automatically adjusted.

Optionally, the image generating device includes a microscope and a camera, and the parameter information includes at least one of an imaging mode, a microscope parameter, and a shooting parameter; the first determination unit is used to perform at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

Optionally, the second acquisition module further includes: a receiving unit, configured to receive a second instruction, wherein the second instruction indicates that a parameter setting of the image generating device has been successfully adjusted.

Optionally, the second acquisition module also includes: a second determination unit, used to determine whether the parameter setting of the image generating device is effective; the second acquisition module is used to use the adjusted image generating device to acquire a single-channel image corresponding to the sample to be observed after the parameter setting of the image generating device is effective.

Optionally, the device further includes: a storage module, configured to store parameter information respectively corresponding to the more than one single-channel images and image processing information corresponding to the second image as second metadata information corresponding to the second image.

Optionally, the device also includes: a storage module, used to store parameter information corresponding to the more than one single-channel images, image processing information corresponding to the second image, and image processing information corresponding to one or more single-channel images constituting the second image as second metadata information corresponding to the second image.

A fourth aspect of the present disclosure provides an image reproduction device, the device comprising: a first acquisition module, used to obtain first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information includes parameter information corresponding to the more than one single-channel images and image processing information corresponding to one or more single-channel images constituting the first image; a second acquisition module, used to obtain, for a sample to be observed, first metadata information corresponding to the sample to be observed based on the parameter information corresponding to the more than one single-channel images. a processing module, used to perform image processing on the one or more single-channel images corresponding to the sample to be observed, based on the image processing information respectively corresponding to the one or more single-channel images constituting the first image; and a generating module, used to superimpose the more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

Optionally, the image processing includes artificial intelligence (AI) analysis, the image processing information includes AI analysis targets and information about the AI analysis tool, and the processing module is used to: obtain the AI analysis tool based on the information about the AI analysis tool; and use the AI analysis tool to perform image processing to achieve the AI analysis target.

Optionally, the AI analysis tool includes one or more AI analysis models; the information about the AI analysis tool includes at least one of the following: used to obtain relevant information for each AI analysis model; each AI analysis model.

Optionally, the information about the AI analysis tool also includes feature information of each AI analysis model.

Optionally, the AI analysis tool includes multiple AI analysis models; the device also includes: a determination module, used to determine a target AI analysis model from the multiple AI analysis models; the processing module is used to: use the target AI analysis model to perform image processing to achieve the AI analysis goal.

Optionally, the determination module is used to: receive AI analysis requirements input by a user; and determine the target AI analysis model based on the AI analysis requirements and feature information of each AI analysis model.

Optionally, the determination module is used to: display information about the multiple AI analysis models; and determine a target AI analysis model from the multiple AI analysis models based on a user's selection input.

Optionally, the AI analysis objectives include at least one of the following: determining cell confluence; determining cell count; or determining cell transfection rate.

Optionally, the device also includes: a transceiver module: used to output a first prompt message, wherein the first prompt message is used to prompt a user to confirm whether to generate the second image for the sample to be observed; and used to receive a first instruction, wherein the first instruction instructs the user to confirm the generation of the second image for the sample to be observed.

Optionally, the second acquisition module includes: an adjustment unit for adjusting parameter settings of an image generating device for each single-channel image constituting the first image based on parameter information corresponding to the single-channel image; and an acquisition unit for using the adjusted image generating device to acquire the single-channel image corresponding to the sample to be observed.

Optionally, the second acquisition module also includes: a first determination unit, used to determine whether the parameter settings of the image generating device can be adjusted based on the parameter information corresponding to the single-channel image; and the adjustment unit, used to adjust the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image when the parameter settings of the image generating device can be adjusted.

Optionally, the second acquisition module further includes: an output unit, configured to output second prompt information when parameter settings of the image generating device cannot be adjusted, wherein the second prompt information is configured to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Optionally, the second prompt information further includes guidance information for guiding the user to manually adjust the parameter that cannot be automatically adjusted.

Optionally, the image generating device includes a microscope and a camera, and the parameter information includes at least one of an imaging mode, a microscope parameter, and a shooting parameter; the first determination unit is used to perform at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

Optionally, the second acquisition module further includes: a receiving unit, configured to receive a second instruction, wherein the second instruction indicates that a parameter setting of the image generating device has been successfully adjusted.

Optionally, the second acquisition module also includes: a second determination unit, used to determine whether the parameter setting of the image generating device is effective; the second acquisition module is used to use the adjusted image generating device to acquire a single-channel image corresponding to the sample to be observed after the parameter setting of the image generating device is effective.

Optionally, the device also includes: a storage module, used to store parameter information corresponding to the more than one single-channel images, image processing information corresponding to the second image, and image processing information corresponding to one or more single-channel images constituting the second image as second metadata information corresponding to the second image.

The fifth aspect embodiment of the present disclosure provides an image reproduction device, comprising: a processor; a memory for storing instructions executable by the processor; wherein, when the instructions are executed by the processor, the processor is used to execute the image reproduction method described in the first or second aspect embodiment above.

A sixth aspect of the present disclosure provides a computer-readable storage medium having instructions stored thereon. When the instructions When executed by a processor, the image reproduction method described in the first or second aspect embodiments can be implemented.

The embodiment of the present disclosure provides an image reproduction method and device, by obtaining first metadata information corresponding to a first image, for a sample to be observed, based on parameter information respectively corresponding to more than one single-channel images constituting the first image included in the first metadata information, respectively obtaining more than one single-channel images corresponding to the sample to be observed, superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed, and for the second image, based on the image processing information included in the first metadata information, performing image processing, it is possible to automatically obtain a corresponding superimposed image (i.e., the second image) for the sample to be observed based on the current superimposed image (i.e., the first image). Since the multiple single-channel images constituting the newly generated superimposed image are obtained in the same manner as the multiple single-channel images constituting the current superimposed image and are processed in the same image processing manner as the newly generated superimposed image, the newly generated superimposed image has the same image quality as the current superimposed image and is expected to have a similar image processing effect. Therefore, the present disclosure provides a technical solution, which enables a user to reproduce a superimposed image with the same image quality for the sample to be observed based on an existing superimposed image and obtain a desired image processing result.

Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

FIG1 is a schematic flow chart of an image reproduction method according to an embodiment of the present disclosure;

FIG2 is a schematic flow chart of another image reproduction method according to an embodiment of the present disclosure;

FIG3 is a schematic flow chart of another image reproduction method according to an embodiment of the present disclosure;

FIG4 is a schematic flow chart of another image reproduction method according to an embodiment of the present disclosure;

FIG5 is a schematic flow chart of another image reproduction method according to an embodiment of the present disclosure;

FIG6 is a schematic structural diagram of an image reproduction device provided by an embodiment of the present disclosure;

FIG7 is a schematic structural diagram of another image reproduction device provided by an embodiment of the present disclosure;

FIG8 is a schematic structural diagram of another image reproduction device provided by an embodiment of the present disclosure;

FIG9 is a schematic structural diagram of another image reproduction device provided by an embodiment of the present disclosure;

FIG10 is a schematic structural diagram of an image reproduction device provided by an embodiment of the present disclosure;

FIG11 is a schematic structural diagram of another image reproduction device provided by an embodiment of the present disclosure;

FIG12 is a schematic structural diagram of another image reproduction device provided by an embodiment of the present disclosure;

FIG13 is a schematic structural diagram of another image reproduction device provided by an embodiment of the present disclosure;

FIG. 14 is a schematic diagram of the structure of a system for implementing an image reproduction method provided in an embodiment of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

With the development of image processing and analysis technology, superimposed images composed of multiple single-channel images have become mainstream. For example, when observing a sample, multiple single-channel images of the sample are usually obtained, and the multiple single-channel images are superimposed to obtain a superimposed image so that the operator can obtain sufficient information from the superimposed image. In the prior art, in order to obtain a superimposed image, the user is first required to obtain each single-channel image, and then superimpose them to obtain a superimposed image. This requires the user to understand the various parameters used by the image generation device when obtaining each single-channel image, and perform various complex setting operations to obtain each single-channel image.

To this end, the present application provides an image reproduction method, device and computer-readable storage medium, which are used to simply and automatically generate an overlay image with the same image quality for a sample to be observed based on an existing overlay image and obtain a desired image processing result, so that the user does not need to understand the various parameters used by the image generation device when acquiring each single-channel image and perform complex setting operations to obtain the desired image processing result.

The image reproduction method and device provided by the present application are described in detail below with reference to the accompanying drawings.

FIG1 is a flow chart of an image reproduction method according to an embodiment of the present disclosure. As shown in FIG1 , the method may be performed by an image reproduction device, and the method may include but is not limited to the following steps:

Step S101: obtaining first metadata information corresponding to a first image.

In some embodiments, the first image is composed of more than one single-channel image, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image.

The first image may be a superimposed image stored in a gallery of the image reproduction device, or may be a superimposed image received from other external devices. The image processing information is information related to image processing.

Among them, the superimposed image refers to an image composed of multiple single-channel images superimposed, wherein the single-channel image can be an image of various styles, such as a color image captured by a conventional optical camera, an infrared image captured by an infrared camera, or a fluorescent image generated with the help of a fluorescence microscope.

The first metadata information corresponding to the first image may be stored in the gallery in association with the first image, or may also be stored in the first image. The first metadata information includes parameter information corresponding to a plurality of single-channel images constituting the first image, and image processing information corresponding to the first image.

The parameter information corresponding to the single-channel image may include parameters for acquiring the single-channel image. For example, when the single-channel image is an image captured by a conventional optical camera, the parameter information corresponding to the single-channel image may include shooting parameters of the optical camera when capturing the single-channel image, wherein the shooting parameters may include exposure time, focus mode, etc. For another example, when the single-channel image is a fluorescent image generated by a fluorescent microscope, the parameter information corresponding to the single-channel image may include the imaging mode of the fluorescent microscope, and microscope parameters, wherein the microscope parameters may include objective lens magnification, working distance, etc.

Step S102 : for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtain more than one single-channel images corresponding to the sample to be observed.

After the first metadata information is obtained, more than one single-channel image corresponding to the sample to be observed may be respectively obtained based on the parameter information corresponding to each single-channel image constituting the first image and included in the first metadata information.

That is, the same number of single-channel images corresponding to the samples to be observed are acquired according to the number of single-channel images constituting the first image, and each single-channel image corresponding to the sample to be observed is acquired in the same way as the single-channel images of the first image are acquired.

For example, the first image is composed of three single-channel images, namely, the first single-channel image Image1, the second single-channel image Image2, and the third single-channel image Image3; the first metadata information obtained includes parameter information Parameter1 corresponding to the first single-channel image Image1, parameter information Parameter2 corresponding to the second single-channel image Image2, and parameter information Parameter3 corresponding to the third single-channel image Image3. Then, for the sample to be observed, the first single-channel image Image1' corresponding to the sample to be observed can be obtained based on the parameter information Parameter1, the second single-channel image Image2' corresponding to the sample to be observed can be obtained based on the parameter information Parameter2, and the third single-channel image Image3' corresponding to the sample to be observed can be obtained based on the parameter information Parameter3.

In some embodiments, for the sample to be observed, based on parameter information corresponding to the more than one single-channel images, respectively acquiring more than one single-channel images corresponding to the sample to be observed includes: for each single-channel image constituting the first image, based on the parameter information corresponding to the single-channel image, adjusting the parameter setting of the image generating device; and acquiring the single-channel image corresponding to the sample to be observed using the adjusted image generating device.

Specifically, in order to obtain each single-channel image corresponding to the sample to be observed, it is necessary to adjust the parameter settings of the image generation device based on the obtained parameter information corresponding to each single-channel image of the first image, and use the adjusted image generation device to obtain each single-channel image of the sample to be observed. The image generation device may be part of or connected to an image reproduction device that performs the image reproduction method.

For example, the obtained parameter information is Parameter1, Parameter2, and Parameter3, wherein Parameter1 includes the exposure time and focus mode of the camera, Parameter2 includes the exposure time and focus mode of the camera, the imaging mode of the fluorescence microscope, and microscope parameters, and Parameter3 includes the exposure time and focus mode of the camera, the imaging mode of the fluorescence microscope, and microscope parameters (the parameters included in Parameter3 may be the same as or different from the parameters included in Parameter2); then the parameter settings of the image generating device (here the camera) may be adjusted based on Parameter1, and the first single-channel image corresponding to the sample to be observed may be obtained using the adjusted camera; the parameter settings of the image generating device (here the camera and the fluorescence microscope) may be adjusted based on Parameter2, and the second single-channel image corresponding to the sample to be observed may be obtained using the adjusted camera and the fluorescence microscope; and the parameter settings of the image generating device (here the camera and the fluorescence microscope) may be adjusted based on Parameter3, and the third single-channel image corresponding to the sample to be observed may be obtained using the adjusted camera and the fluorescence microscope.

In some embodiments, before adjusting the parameter settings of the image generating device, the method further includes: determining whether the parameter settings of the image generating device can be adjusted based on the parameter information corresponding to the single-channel image. The adjusting the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image includes: When the parameter setting of the image generating device can be adjusted, the parameter setting of the image generating device is adjusted based on the parameter information corresponding to the single-channel image.

In some embodiments, the method further includes: when the parameter setting of the image generating device cannot be adjusted, outputting second prompt information, wherein the second prompt information is used to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Since some parameter settings of the image generating device may not be automatically adjusted in some cases, and need to be adjusted manually by the user, for example, the objective lens magnification of the fluorescence microscope needs to be adjusted by the user manually rotating the knob, in order to avoid the problem caused by the parameter settings of the image generating device not being automatically adjusted (such as the inability to obtain a single-channel image with the desired parameter settings), before adjusting the parameter settings of the image generating device, it is necessary to determine whether the parameter settings of the image generating device can be adjusted based on the parameter information. If the parameter settings of the image generating device can be adjusted based on the parameter information, the parameter settings of the image generating device are adjusted accordingly; if the parameter settings of the image generating device cannot be adjusted based on the parameter information, a second prompt information can be output to prompt the user that the parameter settings of the image generating device cannot be adjusted accordingly. Among them, the second prompt information can indicate to the user which parameters of the image generating device cannot be automatically adjusted. For example, when the output second prompt information indicates the objective lens magnification, the user can be informed according to the prompt information that the objective lens magnification needs to be manually adjusted.

In some embodiments, the second prompt information further includes guidance information for guiding the user to manually adjust the parameters that cannot be automatically adjusted.

Since some parameter settings of the image generating device are usually complicated, the user may not understand how to set the parameters of the image generating device. Therefore, in order to help the user appropriately adjust the parameter settings of the image generating device, the second prompt information can also include guidance information, which can guide the user to manually adjust the parameters that cannot be automatically adjusted.

For example, the second prompt information indicates that the objective lens magnification cannot be adjusted automatically, and the second prompt information may include guide information, which is used to indicate to the user the specific operation instructions for adjusting the objective lens magnification, so that the user can easily manually adjust the objective lens magnification according to the guide information.

In some embodiments, the second prompt information may be provided in the form of audio output, text output, or any other output.

In some embodiments, the image generating device includes a microscope and a camera, and the parameter information includes at least one of an imaging mode, a microscope parameter, and a shooting parameter; determining whether the parameter settings of the image generating device can be adjusted includes at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

In order to obtain the superimposed image corresponding to the sample to be observed, it is necessary to use an image generation device to obtain multiple single-channel images constituting the superimposed image. The single-channel image can be a color image, an infrared image, or a fluorescence image, etc. When the single-channel image is a color image or an infrared image, in order to obtain the single-channel image, the image generation device includes a camera; when the single-channel image is a fluorescence image, in order to obtain the single-channel image, the image generation device includes a microscope and a camera.

Accordingly, the parameter information may include at least one of an imaging mode of the microscope, microscope parameters, and shooting parameters of the camera.

When determining whether the parameter settings of the image generating device can be adjusted, it is determined based on the parameter information whether the parameter settings of the microscope and/or camera of the image generating device can be adjusted. For example, if the parameter information includes the shooting parameters of the camera, it is determined whether the camera can be adjusted according to the shooting parameters. For another example, if the parameter information includes the imaging mode of the microscope, the microscope parameters, and the shooting parameters of the camera, it is determined whether the microscope can be adjusted according to the microscope parameters, whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information, and whether the camera can be adjusted according to the shooting parameters.

In some embodiments, before using the adjusted image generating device to acquire a single-channel image corresponding to the sample to be observed, the method further includes: receiving a second instruction, wherein the second instruction indicates that the parameter setting of the image generating device has been successfully adjusted.

When adjusting the parameter settings of the image generating device based on the parameter information, an unexpected situation may occur that the parameter settings of the image generating device are not successfully adjusted. If the image generating device is used to obtain a single-channel image when the parameter settings of the image generating device are not successfully adjusted, the obtained single-channel image may be inconsistent with the expected single-channel image, that is, it cannot meet the user's needs.

In order to avoid the above problems, the present application provides a user interaction function. Before using the adjusted image generation device to obtain a single-channel image corresponding to the sample to be observed, user confirmation is required. After receiving a confirmation instruction indicating that the parameter settings of the image generation device have been successfully adjusted, the adjusted image generation device is used to obtain a single-channel image corresponding to the sample to be observed.

In some embodiments, in order to prompt the user to confirm the adjustment of the parameter setting of the image generating device, the method The method further includes: outputting prompt information for instructing a user to confirm the adjustment of the parameter setting of the image generating device.

For example, after adjusting the parameter settings of the image generating device based on the parameter information, a prompt message "Please confirm whether the XX parameter of the image generating device has been successfully adjusted to XX" is output, and the user confirms whether the corresponding parameters of the image generating device have been successfully adjusted to the expected values after receiving the prompt message. If the corresponding parameters of the image generating device have been successfully adjusted to the expected values, a confirmation instruction can be entered; otherwise, the user needs to manually adjust the parameters that have not been successfully adjusted, and enter a confirmation instruction after adjusting the corresponding parameters of the image generating device to the expected values.

In some embodiments, before using the adjusted image generation device to obtain the single-channel image corresponding to the sample to be observed, the method further includes: determining whether the parameter setting of the image generation device is effective. The using the adjusted image generation device to obtain the single-channel image corresponding to the sample to be observed includes: after the parameter setting of the image generation device is effective, using the adjusted image generation device to obtain the single-channel image corresponding to the sample to be observed.

After certain parameter settings of the image generation device are adjusted, it takes some time for them to take effect. In particular, in order to obtain a superimposed image corresponding to the sample to be observed, it is necessary to use the image generation device to obtain multiple single-channel images that constitute the superimposed image, and the parameter information corresponding to each single-channel image may have a large difference, which means that the parameter settings of the image generation device need to be greatly adjusted, and the adjusted parameter settings take some time to take effect.

For example, the exposure time of the camera shooting parameter corresponding to the first single-channel image is 10s, while the exposure time of the camera shooting parameter corresponding to the second single-channel image is 100s. After using the camera to obtain the first single-channel image, it is necessary to reset the exposure time of the camera, and wait for a period of time until the adjusted exposure time takes effect before using the camera again to obtain the second single-channel image. If the second single-channel image is obtained immediately after the exposure time is adjusted (that is, before the adjusted exposure time takes effect), the exposure time used at this time is still the original exposure time (10s) instead of the adjusted exposure time (100s), then the second single-channel image obtained may be inconsistent with the expected single-channel image, that is, it cannot meet the user's needs.

In order to avoid the above problems, before using the adjusted image generating device to obtain the single-channel image corresponding to the sample to be observed, it is also determined whether the parameter setting of the image generating device is effective. After determining that the parameter setting of the image generating device is effective, the adjusted image generating device is used to obtain the single-channel image corresponding to the sample to be observed.

Step S103 , superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

After acquiring each single-channel image corresponding to the sample to be observed, the acquired multiple single-channel images are superimposed to obtain a superimposed image corresponding to the sample to be observed.

For example, after obtaining the first single-channel image Image1’, the second single-channel image Image2’, and the third single-channel image Image3’ corresponding to the sample to be observed, the first single-channel image Image1’, the second single-channel image Image2’, and the third single-channel image Image3’ are superimposed to generate a superimposed image corresponding to the sample to be observed.

After generating the second image, the image generation and reproduction device may present the generated second image to the user.

Step S104: performing image processing on the second image based on the image processing information.

The first metadata information also includes image processing information corresponding to the first image, so after the second image is generated, image processing can be performed on the second image according to the image processing information.

In some embodiments, the image processing may include artificial intelligence (AI) analysis, and the image processing information may include an AI analysis target and information about an AI analysis tool. Based on the image processing information, performing image processing includes: acquiring an AI analysis tool according to the information about the AI analysis tool; and using the AI analysis tool to perform image processing to achieve the AI analysis target.

In some embodiments, the AI analysis tool includes one or more AI analysis models, and the information about the AI analysis tool includes at least one of the following: information used to obtain relevant information of the AI analysis model; or an AI analysis model.

When performing AI analysis on an image, an AI analysis model is usually used to achieve the AI analysis goal. In order to obtain the AI analysis model used for AI analysis, relevant information for obtaining the AI analysis model can be included in the image processing information, and the corresponding AI analysis model can be obtained based on the relevant information.

Among them, the relevant information used to obtain the AI analysis model may be, for example, the storage address of the AI analysis model, the name of the AI analysis model, the name of the cell to which the AI analysis model is applied, the index of the AI analysis model, etc.

However, an AI analysis model is usually generated through training. For example, the initial AI analysis model can usually be continuously trained to obtain an updated AI analysis model. In other words, the AI analysis model is not always the same. After the first image is processed using the initial AI analysis model, the storage address of the initial AI analysis model can be used to obtain relevant information of the AI analysis model. However, if the AI analysis model is subsequently updated, the updated AI analysis model may replace the initial AI analysis model and be stored at the corresponding storage address. In this case, if the AI analysis model is still obtained based on the storage address of the AI analysis model, the obtained AI analysis model is the updated AI analysis model, not the initial AI analysis model used for AI analysis of the first image. AI analysis model.

In order to ensure that the AI analysis model obtained according to the image processing information corresponding to the first image is the AI analysis model used for AI analysis of the first image, the AI analysis model itself may be directly included in the image processing information corresponding to the first image. The AI analysis model may be stored in a document form.

In some embodiments, the information about the AI analysis tool also includes feature information of each AI analysis model.

As described above, the initial AI analysis model can be continuously trained to obtain an updated AI analysis model, so in addition to the initial AI analysis model, multiple updated AI analysis models can be obtained through different training processes. The updated AI analysis models obtained through different training processes are different.

For example, after using the initial AI analysis model to analyze an image, the user finds that the initial AI analysis model fails to successfully identify some separated cells in the image (for example, identifies multiple separated cells as a single cell) or fails to identify some cells in the image (for example, identification errors occur). Therefore, the initial AI analysis model cannot meet the user's needs. In this case, the user usually annotates the image (for example, annotates the separated cells, annotates the missed cells), and uses the annotated images to update the initial AI analysis model through training to obtain an updated AI analysis model.

For example, after using the initial AI analysis model to analyze an image, the user finds that the initial AI analysis model fails to successfully distinguish between the image background and the cells in the image, and thus the initial AI analysis model cannot meet the user's needs. In this case, the user usually annotates the image (for example, annotating the background and cells with different labels) and uses the annotated image to update the initial AI analysis model through training to obtain an updated AI analysis model.

In order to identify the differences between different AI analysis models, the information about the AI analysis tool also includes feature information of each AI analysis model, such as feature information that can identify the problems that the AI analysis model is good at handling, the training goals when training the AI analysis model, etc. For example, some AI analysis models are better at distinguishing between background and cells, while some AI analysis models are better at identifying separated cells, etc.

In some embodiments, when the AI analysis tool includes multiple AI analysis models, after acquiring the AI analysis tool according to the information about the AI analysis tool, the method further includes: determining a target AI analysis model from the multiple AI analysis models. The using the AI analysis tool to perform image processing to achieve the AI analysis goal includes: using the target AI analysis model to perform image processing to achieve the AI analysis goal.

When multiple AI analysis models are acquired based on information about the AI analysis tool, a target AI analysis model can be determined from the multiple AI analysis models, and the target AI analysis model can be used to perform image processing to achieve the AI analysis goal.

In some embodiments, determining a target AI analysis model from the multiple AI analysis models includes: receiving an AI analysis requirement input by a user; and determining the target AI analysis model based on the AI analysis requirement and feature information of each AI analysis model.

According to an embodiment of the present disclosure, a target AI analysis model can be recommended to a user. Specifically, an AI analysis requirement can be received from a user, such as an AI analysis target that the user expects to achieve, a problem that the user expects to solve, etc., and the image reproduction device determines an AI analysis model whose feature information matches the AI analysis requirement as the target AI analysis model based on the AI analysis requirement and feature information of each AI analysis model.

The user may determine the AI analysis requirement based on the second image. For example, the user may observe the second image based on experience and thereby determine the AI analysis requirement for performing AI analysis on the second image.

In some embodiments, determining a target AI analysis model from the multiple AI analysis models includes: displaying information about the multiple AI analysis models; and determining a target AI analysis model from the multiple AI analysis models based on a user's selection input.

According to an embodiment of the present disclosure, a target AI analysis model can be selected by a user from a plurality of AI analysis models. Specifically, information about a plurality of AI analysis models (such as the name and storage address of each AI analysis model) can be displayed on a display interface, for example, in the form of a list, and the AI analysis model selected by the user can be determined as the target AI analysis model based on the user's selection input.

In some embodiments, the AI analysis objectives include at least one of: determining cell confluence; determining cell count; or determining cell transfection rate.

For example, if the image processing information indicates that the cell transfection rate in the first image is determined using the first AI analysis model, the cell transfection rate in the second image may also be determined using the first analysis model.

In some embodiments, the image generation and reproduction device itself may not have an image processing function. In this case, the image generation and reproduction device may send the image processing information and the second image to an external image processing device. After the external image processing device performs image processing on the second image based on the image processing information, the image processing device returns the processed second image to the image generation and reproduction device. The image generation and reproduction device may present the processed second image to the user.

According to the image reproduction method provided by the embodiment of the present disclosure, by obtaining first metadata information corresponding to a first image; for a sample to be observed, based on parameter information respectively corresponding to more than one single-channel images constituting the first image included in the first metadata information, respectively obtaining more than one single-channel images corresponding to the sample to be observed; superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed, and for the second image, based on the image processing information included in the first metadata information, performing image processing, it is possible to automatically obtain a corresponding superimposed image (i.e., the second image) for the sample to be observed based on the current superimposed image (i.e., the first image), because the multiple single-channel images constituting the newly generated superimposed image are obtained in the same manner as the multiple single-channel images constituting the current superimposed image and are processed in the same image processing manner as the newly generated superimposed image, the newly generated superimposed image has the same image quality as the current superimposed image and is expected to have a similar image processing effect. Thus, the user can reproduce a superimposed image with the same image quality for the sample to be observed based on the existing superimposed image and is expected to have a similar image processing effect.

The information processing method involved in the embodiment of the present disclosure may include at least one of steps S101 to S104. For example, steps S101 to S103 may be implemented as independent embodiments, but are not limited thereto.

In the embodiments of the present disclosure, part or all of the steps and their optional implementations may be arbitrarily combined with part or all of the steps in other embodiments, or may be arbitrarily combined with optional implementations of other embodiments.

FIG2 is a flow chart of another method for reproducing an image according to an embodiment of the present disclosure. As shown in FIG2 , the method may be performed by an image reproducing device, and the method may include but is not limited to the following steps:

Step S201: obtaining first metadata information corresponding to a first image.

In some embodiments, the first image is composed of more than one single-channel image, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image.

For a detailed description of step S201, reference may be made to the description of step S101 in the above embodiment, which will not be repeated here.

Step S202: outputting first prompt information, wherein the first prompt information is used to prompt the user to confirm whether to generate a second image for the sample to be observed.

In some cases, in order to obtain the superimposed image corresponding to the sample to be observed, it is necessary to perform necessary preprocessing on the sample to be observed. For example, in order to obtain the fluorescent image of the sample to be observed, the sample to be observed needs to be stained in advance. In another example, in order to obtain the image of a specific part of the sample to be observed, the sample to be observed needs to be adjusted to a suitable position.

Therefore, in order to obtain the superimposed image corresponding to the sample to be observed, according to this embodiment, after obtaining the first metadata information corresponding to the first image, the first prompt information can be output to prompt the user to confirm whether to generate the superimposed image for the sample to be observed. After the user receives the first prompt information, if it is confirmed to generate the superimposed image for the sample to be observed, it is necessary to complete the preprocessing of the sample to be observed to ensure that the superimposed image corresponding to the sample to be observed can be obtained.

In some embodiments, the first prompt information may include parameter information corresponding to each single-channel image of the first image, so that the user can determine whether to obtain each single-channel image of the sample to be observed based on the corresponding parameter information according to the parameter information corresponding to each single-channel image of the first image.

Step S203, receiving a first instruction, wherein the first instruction instructs the user to confirm generating a second image for the sample to be observed.

If the user confirms to generate a superimposed image for the sample to be observed, a first instruction may be provided.

After receiving the first instruction, the image reproduction device confirms to generate a superimposed image for the sample to be observed based on the first instruction.

Step S204 : for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtain more than one single-channel images corresponding to the sample to be observed.

Step S205 , superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

Step S206: performing image processing on the second image based on the image processing information.

For a detailed description of steps S204-S206, reference may be made to the description of steps S102-S104 in the above embodiment, which will not be repeated here.

According to the image reproduction method provided by the embodiment of the present disclosure, by obtaining first metadata information corresponding to a first image; outputting prompt information for prompting a user to confirm whether to generate a superimposed image for a sample to be observed; receiving a second instruction instructing the user to confirm generating a superimposed image for the sample to be observed; for the sample to be observed, based on parameter information respectively corresponding to more than one single-channel images constituting the first image included in the first metadata information, respectively obtaining more than one single-channel images corresponding to the sample to be observed; superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed, and for the second image, performing image processing based on the image processing information included in the first metadata information, it is possible to automatically obtain a corresponding superimposed image (i.e., the second image) for the sample to be observed based on the current superimposed image (i.e., the first image), because the multiple single-channel images constituting the newly generated superimposed image are obtained and processed in the same manner as the multiple single-channel images constituting the current superimposed image. The newly generated superimposed image is processed in the same image processing manner as the current superimposed image, and the newly generated superimposed image has the same image quality as the current superimposed image and is expected to have a similar image processing effect. Thus, the user can reproduce a superimposed image with the same image quality for the sample to be observed based on the existing superimposed image and obtain the desired image processing result.

The information processing method involved in the embodiment of the present disclosure may include at least one of steps S201 to S206. For example, steps S201-S205 may be implemented as independent embodiments, or steps S201 and S203-S205 may be implemented as independent embodiments, but are not limited thereto.

In the embodiments of the present disclosure, part or all of the steps and their optional implementations may be arbitrarily combined with part or all of the steps in other embodiments, or may be arbitrarily combined with optional implementations of other embodiments.

FIG3 is a flow chart of another method for reproducing an image according to an embodiment of the present disclosure. As shown in FIG3 , the method may be performed by an image reproducing device, and the method may include but is not limited to the following steps:

Step S301: Obtain first metadata information corresponding to a first image.

In some embodiments, the first image is composed of more than one single-channel image, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image.

For a detailed description of step S301, reference may be made to the description of step S101 in the above embodiment, which will not be repeated here.

Step S302 : for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtain more than one single-channel images corresponding to the sample to be observed.

Step S303 , superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

Step S304: performing image processing on the second image based on the image processing information.

For a detailed description of steps S302 - S304 , reference may be made to the description of steps S102 - S104 in the above embodiment, which will not be repeated here.

Step S305 : storing parameter information corresponding to the more than one single-channel images and image processing information corresponding to the second image as second metadata information corresponding to the second image.

After the second image is generated, the parameter information corresponding to each single-channel image constituting the second image and the image processing information corresponding to the second image are stored as second metadata information corresponding to the second image, so that the parameter information corresponding to each single-channel image and the image processing information corresponding to the second image can be obtained based on the second image, thereby realizing image reproduction based on the second image.

According to the image reproduction method provided by the embodiment of the present disclosure, by obtaining first metadata information corresponding to a first image; for a sample to be observed, based on parameter information respectively corresponding to more than one single-channel images constituting the first image included in the first metadata information, respectively obtaining more than one single-channel images corresponding to the sample to be observed; superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed; for the second image, performing image processing based on the image processing information included in the first metadata information; and storing the parameter information respectively corresponding to the more than one single-channel images and the image processing information corresponding to the second image as the second metadata information corresponding to the second image, it is possible to automatically obtain a corresponding superimposed image (i.e., the second image) for the sample to be observed based on the current superimposed image (i.e., the first image), and in addition, it is possible to obtain the parameter information respectively corresponding to each single-channel image and the image processing information corresponding to the second image from the corresponding second metadata information of the second image. Since the multiple single-channel images constituting the newly generated superimposed image are obtained in the same manner as the multiple single-channel images constituting the current superimposed image and are processed in the same image processing manner as the newly generated superimposed image and the current superimposed image, the newly generated superimposed image has the same image quality as the current superimposed image and is expected to have a similar image processing effect. As a result, the user can reproduce a superimposed image with the same image quality for the sample to be observed based on the existing superimposed image and obtain the desired image processing result. In addition, since the corresponding second metadata information of the second image stores parameter information corresponding to each single-channel image and image processing information corresponding to the second image, the user can subsequently achieve image reproduction based on the second image.

The information processing method involved in the embodiment of the present disclosure may include at least one of steps S301 to S305. For example, steps S301-S303 may be implemented as an independent embodiment, or steps S301-S304 may be implemented as an independent embodiment, but are not limited thereto.

In the embodiments of the present disclosure, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations of other embodiments. For example, the above steps S301-S305 may be combined with steps S202-S203, but are not limited thereto.

FIG4 is a flow chart of another method for reproducing an image according to an embodiment of the present disclosure. As shown in FIG4 , the method may be performed by an image reproducing device, and the method may include but is not limited to the following steps:

Step S401: Obtain first metadata information corresponding to a first image.

In some embodiments, the first image is composed of more than one single-channel image, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image.

For a detailed description of step S401, reference may be made to the description of step S101 in the above embodiment, which will not be repeated here.

Furthermore, in some embodiments, the first metadata information further includes image processing information respectively corresponding to one or more single-channel images constituting the first image.

According to an embodiment of the present disclosure, since the first image is a superimposed image composed of multiple single-channel images, in addition to the first image being subjected to image processing, the single-channel images constituting the first image may also be subjected to image processing. Therefore, the first metadata information may also include image processing information corresponding to one or more single-channel images. Among them, each single-channel image constituting the first image may be subjected to image processing, or some single-channel images constituting the first image may be subjected to image processing.

In some embodiments, the first image may not have been image processed, but only one or more single-channel images constituting the first image have been image processed. In this case, the first metadata information does not include image processing information corresponding to the first image, but includes image processing information corresponding to the one or more single-channel images.

Step S402 : for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively obtain more than one single-channel images corresponding to the sample to be observed.

For a detailed description of step S402, reference may be made to the description of step S102 in the above embodiment, which will not be repeated here.

Step S403, performing image processing on more than one single-channel images corresponding to the sample to be observed based on the image processing information; or sending the second image and the image processing information to an external image processing device, and receiving the second image after image processing from the external image processing device.

When the first metadata information also includes image processing information corresponding to one or more single-channel images, after acquiring one or more single channels of the second image, image processing can be performed on the one or more single-channel images of the second image according to the image processing information.

The specific process of performing image processing on each single-channel image according to the image processing information may refer to the process of performing image processing on the second image according to the image processing information, such as the description of step S104, which will not be repeated here.

For example, if the image processing information corresponding to the first single-channel image of the first image indicates the use of the first AI analysis model to determine the cell transfection rate in the first single-channel image, the first analysis model can also be used to determine the cell transfection rate of the first single-channel image of the second image (wherein the first single-channel image of the second image is a single-channel image obtained using the parameter information corresponding to the first single-channel image of the first image); if the image processing information corresponding to the second single-channel image of the first image indicates that the second single-channel image is to be cropped, the second single-channel image of the second image (wherein the second single-channel image of the second image is a single-channel image obtained using the parameter information corresponding to the second single-channel image of the first image) can also be cropped.

In some embodiments, the image generation and reproduction device itself may not have an image processing function. In this case, the image generation and reproduction device may send the image processing information and the second image to an external image processing device. After the external image processing device performs image processing on each single-channel image of the second image based on the image processing information, the image processing device returns the processed second image to the image generation and reproduction device. The image generation and reproduction device may present the processed second image to the user.

In some embodiments, parameter information respectively corresponding to more than one single-channel images constituting the second image, image processing information corresponding to the second image, and image processing information respectively corresponding to one or more single-channel images constituting the second image are stored as second metadata information corresponding to the second image.

Step S404 , superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

Among them, one or more single-channel images among more than one single-channel corresponding to the sample to be observed have been image processed, and the one or more single-channel images that have been image processed can be superimposed with other single-channel images that have not been image processed to obtain a second image, or all more than one single-channel images that have not been image processed can be superimposed to obtain the second image, depending on the specific needs.

Step S405: performing image processing on the second image based on the image processing information.

For a detailed description of steps S404-S405, reference may be made to the description of steps S103-S104 in the above embodiment, which will not be repeated here.

According to the image reproduction method provided by the embodiment of the present disclosure, by obtaining first metadata information corresponding to a first image; for a sample to be observed, based on parameter information respectively corresponding to more than one single-channel images constituting the first image and included in the first metadata information, respectively obtaining more than one single-channel images corresponding to the sample to be observed; superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed; and performing image processing on the second image and/or each of its single-channel images based on the image processing information included in the first metadata information, it is possible to obtain a second image corresponding to the sample to be observed based on the current superimposed image (i.e., the first image). The first image) can automatically obtain a corresponding superimposed image (ie, a second image) for the sample to be observed. In addition, the superimposed image corresponding to the sample to be observed can be automatically processed based on the current superimposed image.

The information processing method involved in the embodiment of the present disclosure may include at least one of steps S401 to S405. For example, steps S401-S404 may be implemented as an independent embodiment, and steps S401-S403 may be implemented as an independent embodiment, but are not limited thereto.

In the embodiments of the present disclosure, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations of other embodiments. For example, the above steps S401-S404 may be combined with steps S202-S203, and steps S401-S404 may be combined with step S305, but are not limited thereto.

FIG5 is a flow chart of another method for reproducing an image according to an embodiment of the present disclosure. As shown in FIG5 , the method may include but is not limited to the following steps:

Step S501, obtaining a first image from a gallery.

Step S502: open the first image.

Step S503: the user clicks the image reproduction button.

Step S504: adjusting various parameter settings of the image generation device.

Step S505, determining whether the objective lens of the microscope meets the parameter setting, if the objective lens does not meet the parameter setting, executing step S506, otherwise executing step S507.

Step S506, guiding the user to adjust the objective lens.

Step S507 , the user confirms that various parameter settings of the image generation device have been adjusted.

Step S508, return to the home page and prompt the user that the image reproduction is ready.

Step S509 , in response to the user confirming to perform image reproduction, obtaining each single-channel image corresponding to the sample to be observed and generating a superimposed image corresponding to the sample to be observed based on each single-channel image for display.

Step S510: Perform AI analysis on the superimposed image corresponding to the sample to be observed using the same AI analysis model as that for the first image.

Step S511, displaying the image processed by AI analysis.

The embodiments of the present disclosure also propose a device for implementing any of the above methods. For example, a device is proposed, and the above device includes units or modules for implementing each step in any of the above methods.

It should be understood that the division of the various units or modules in the device is only a division of logical functions, which can be fully or partially integrated into one physical entity or physically separated in actual implementation. In addition, the units or modules in the device can be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above-mentioned device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory inside the device or a memory outside the device. Alternatively, the units or modules in the device may be implemented in the form of hardware circuits, and the functions of some or all of the units or modules may be implemented by designing the hardware circuits. The hardware circuits may be understood as one or more processors; for example, in one implementation, the hardware circuits are application-specific integrated circuits (ASICs), and the functions of some or all of the above units or modules may be implemented by designing the logical relationship of the components in the circuits; for another example, in another implementation, the hardware circuits may be implemented by programmable logic devices (PLDs), and Field Programmable Gate Arrays (FPGAs) may be used as an example, which may include a large number of logic gate circuits, and the connection relationship between the logic gate circuits may be configured by configuring the configuration files, thereby implementing the functions of some or all of the above units or modules. All units or modules of the above devices may be implemented in the form of software called by the processor, or in the form of hardware circuits, or in the form of software called by the processor, and the remaining part may be implemented in the form of hardware circuits.

In the embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor may be a circuit with instruction reading and execution capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which may be understood as a microprocessor), or a digital signal processor (DSP); in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the above hardware circuit may be fixed or reconfigurable, such as a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration may be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it may also be a hardware circuit designed for artificial intelligence, which may be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DLP), or a computer programmable logic device (CLP). DPU) etc.

FIG6 is a schematic diagram of the structure of an image reproduction device provided by an embodiment of the present disclosure; as shown in FIG6 , the image reproduction device 500 may include a first acquisition module 501 , a second acquisition module 502 , a generation module 503 and a processing module 504 .

The first acquisition module 501 is used to obtain first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image.

The second acquisition module 502 is used for respectively acquiring, for the sample to be observed, more than one single-channel images corresponding to the sample to be observed based on the parameter information respectively corresponding to the more than one single-channel images.

The generating module 503 is used to superimpose more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

The processing module 504 is configured to perform image processing on the second image based on the image processing information.

Optionally, the first metadata information also includes image processing information corresponding to the more than one single-channel images respectively, and the processing module 504 is further used to: perform image processing on the more than one single-channel images corresponding to the sample to be observed based on the image processing information.

Optionally, the image processing includes artificial intelligence (AI) analysis, the image processing information includes an AI analysis target and information about the AI analysis tool, and the processing module 504 is used to: obtain the AI analysis tool based on the information about the AI analysis tool; and use the AI analysis tool to perform image processing to achieve the AI analysis target.

Optionally, the AI analysis tool includes one or more AI analysis models; the information about the AI analysis tool includes at least one of the following: used to obtain relevant information for each AI analysis model; each AI analysis model.

Optionally, the information about the AI analysis tool also includes feature information of each AI analysis model.

Optionally, the AI analysis tool includes multiple AI analysis models; referring to FIG7 , optionally, the device further includes: a determination module 505 for determining a target AI analysis model from the multiple AI analysis models; the processing module 504 is used to: use the target AI analysis model to perform image processing to achieve the AI analysis goal.

Optionally, the determination module 505 is used to: receive an AI analysis requirement input by a user; and determine the target AI analysis model according to the AI analysis requirement and feature information of each AI analysis model.

Optionally, the determination module 505 is used to: display information about the multiple AI analysis models; and determine a target AI analysis model from the multiple AI analysis models based on a user's selection input.

Optionally, the AI analysis objectives include at least one of the following: determining cell confluence; determining cell count; or determining cell transfection rate.

Referring to Figure 8, optionally, the device also includes: a transceiver module 506: used to output a first prompt message, wherein the first prompt message is used to prompt the user to confirm whether to generate the second image for the sample to be observed; and used to receive a first instruction, wherein the first instruction instructs the user to confirm the generation of the second image for the sample to be observed.

Optionally, the second acquisition module 502 includes: an adjustment unit for adjusting parameter settings of an image generating device for each single-channel image constituting the first image based on parameter information corresponding to the single-channel image; and an acquisition unit for using the adjusted image generating device to acquire the single-channel image corresponding to the sample to be observed.

Optionally, the second acquisition module 502 also includes: a first determination unit, used to determine whether the parameter settings of the image generating device can be adjusted based on the parameter information corresponding to the single-channel image; and the adjustment unit, used to adjust the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image when the parameter settings of the image generating device can be adjusted.

Optionally, the second acquisition module 502 further includes: an output unit, configured to output second prompt information when the parameter setting of the image generating device cannot be adjusted, wherein the second prompt information is used to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Optionally, the second prompt information further includes guidance information for guiding the user to manually adjust the parameters that cannot be automatically adjusted.

Optionally, the image generating device includes a microscope and a camera, and the parameter information includes at least one of an imaging mode, a microscope parameter, and a shooting parameter; the first determination unit is used to perform at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

Optionally, the second acquisition module 502 further includes: a receiving unit, configured to receive a second instruction, wherein the second instruction indicates that the parameter setting of the image generating device has been successfully adjusted.

Optionally, the second acquisition module 502 also includes: a second determination unit, used to determine whether the parameter setting of the image generating device is effective; the second acquisition module 502 is used to use the adjusted image generating device to obtain a single-channel image corresponding to the sample to be observed after the parameter setting of the image generating device is effective.

Referring to FIG. 9 , optionally, the image reproduction device 500 further includes: a storage module 507 for storing parameter information corresponding to the more than one single-channel images and image processing information corresponding to the second image as second metadata information corresponding to the second image.

Optionally, the image reproduction device 500 also includes: a storage module 507, which is used to store parameter information corresponding to the more than one single-channel images, image processing information corresponding to the second image, and image processing information corresponding to the more than one single-channel images as second metadata information corresponding to the second image.

FIG10 is a schematic diagram of the structure of an image reproduction device provided by an embodiment of the present disclosure; as shown in FIG10 , the image reproduction device 600 may include a first acquisition module 601 , a second acquisition module 602 , a processing module 603 and a generation module 604 .

The first acquisition module 601 is used to obtain first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information includes parameter information corresponding to the more than one single-channel images and image processing information corresponding to one or more single-channel images constituting the first image.

The second acquisition module 602 is used to respectively acquire, for the sample to be observed, more than one single-channel images corresponding to the sample to be observed based on the parameter information respectively corresponding to the more than one single-channel images.

The processing module 603 is used to perform image processing on one or more single-channel images corresponding to the sample to be observed based on image processing information corresponding to one or more single-channel images constituting the first image.

The generating module 604 is configured to superimpose more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed.

Optionally, the image processing includes artificial intelligence (AI) analysis, the image processing information includes an AI analysis target and information about the AI analysis tool, and the processing module 603 is used to: obtain the AI analysis tool based on the information about the AI analysis tool; and use the AI analysis tool to perform image processing to achieve the AI analysis target.

Optionally, the AI analysis tool includes one or more AI analysis models; the information about the AI analysis tool includes at least one of the following: used to obtain relevant information for each AI analysis model; each AI analysis model.

Optionally, the information about the AI analysis tool also includes feature information of each AI analysis model.

Optionally, the AI analysis tool includes multiple AI analysis models; referring to FIG11 , optionally, the device further includes: a determination module 605 for determining a target AI analysis model from the multiple AI analysis models; the processing module 603 is used to: use the target AI analysis model to perform image processing to achieve the AI analysis goal.

Optionally, the determination module 605 is used to: receive AI analysis requirements input by a user; and determine the target AI analysis model based on the AI analysis requirements and feature information of each AI analysis model.

Optionally, the determination module 605 is used to: display information about the multiple AI analysis models; and determine a target AI analysis model from the multiple AI analysis models based on a user's selection input.

Optionally, the AI analysis objectives include at least one of the following: determining cell confluence; determining cell count; or determining cell transfection rate.

Referring to Figure 12, optionally, the device also includes: a transceiver module 606: used to output a first prompt message, wherein the first prompt message is used to prompt the user to confirm whether to generate the second image for the sample to be observed; and used to receive a first instruction, wherein the first instruction instructs the user to confirm the generation of the second image for the sample to be observed.

Optionally, the second acquisition module 602 includes: an adjustment unit, used to adjust parameter settings of an image generating device for each single-channel image constituting the first image based on parameter information corresponding to the single-channel image; and an acquisition unit, used to use the adjusted image generating device to acquire the single-channel image corresponding to the sample to be observed.

Optionally, the second acquisition module 602 also includes: a first determination unit, used to determine whether the parameter settings of the image generating device can be adjusted based on the parameter information corresponding to the single-channel image; and the adjustment unit, used to adjust the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image when the parameter settings of the image generating device can be adjusted.

Optionally, the second acquisition module 602 further includes: an output unit, configured to output second prompt information when parameter settings of the image generating device cannot be adjusted, wherein the second prompt information is configured to indicate to the user the parameters of the image generating device that cannot be automatically adjusted.

Optionally, the second prompt information further includes guidance information for guiding the user to manually adjust the parameter that cannot be automatically adjusted.

Optionally, the image generating device includes a microscope and a camera, and the parameter information includes imaging mode, microscope parameters, At least one of the shooting parameters; the first determining unit is used to perform at least one of the following: determining whether the microscope can be adjusted according to the microscope parameters included in the parameter information; determining whether the imaging mode of the microscope can be adjusted according to the imaging mode included in the parameter information; determining whether the camera can be adjusted according to the shooting parameters included in the parameter information.

Optionally, the second acquisition module 602 further includes: a receiving unit, configured to receive a second instruction, wherein the second instruction indicates that the parameter setting of the image generating device has been successfully adjusted.

Optionally, the second acquisition module 602 also includes: a second determination unit, used to determine whether the parameter setting of the image generating device is effective; the second acquisition module 602 is used to use the adjusted image generating device to obtain a single-channel image corresponding to the sample to be observed after the parameter setting of the image generating device is effective.

Referring to Figure 13, optionally, the device 600 also includes: a storage module 607, used to store parameter information corresponding to the more than one single-channel images, image processing information corresponding to the second image, and image processing information corresponding to one or more single-channel images constituting the second image as second metadata information corresponding to the second image.

Some embodiments relate to microscopes. FIG. 14 shows a schematic diagram of a system 900 configured to perform the methods described herein. System 900 includes a microscope 910, a camera 920, and a computer system 930. The camera is configured to capture non-fluorescent images, the microscope 910 is configured to acquire fluorescent images, and is connected to the computer system 930. The computer system 930 is configured to perform at least a portion of the methods described herein. The computer system 930 may be configured to execute a machine learning algorithm. The computer system 930 and the microscope 910 and the camera 920 may be separate entities, but may also be integrated into a common housing. The computer system 930 may be part of a central processing system of the microscope 910 and/or the computer system 930 and the camera 920 may be part of a subcomponent of the microscope 910, such as a sensor, actuator, camera, or lighting unit of the microscope 910.

The computer system 930 may be a local computer device (e.g., a personal computer, notebook, tablet computer, or mobile phone) having one or more processors and one or more storage devices, or may be a distributed computer system (e.g., having one or more processors and one or more storage devices distributed in various locations, such as a local client and/or one or more remote server sites and/or data centers). The computer system 930 may include any circuit or combination of circuits. In one embodiment, the computer system 930 may include one or more processors that may be of any type. As used in the present invention, a processor may refer to any type of computing circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, a field programmable gate array (FPGA) such as a microscope or a microscope component (e.g., a camera), or any other type of processor or processing circuit. Other types of circuits that may be included in the computer system 930 may be custom circuits, application specific integrated circuits (ASICs), etc., such as one or more circuits (e.g., communication circuits) used in wireless devices such as mobile phones, tablet computers, laptops, two-way radios, and similar electronic systems. The computer system 930 may include one or more storage devices, which may include one or more storage elements suitable for a particular application, such as main memory in the form of random access memory (RAM), one or more hard disk drives, and/or one or more drives for handling removable media such as compact disks (CDs), flash memory cards, digital video disks (DVDs), etc. The computer system 930 may also include a display device, one or more speakers, and a keyboard and/or controller, which may include a mouse, trackball, touch screen, voice recognition device, or any other device that allows a system user to input information to or receive information from the computer system 930.

Some or all of the method steps may be performed by (or using) a hardware device (e.g., a processor, a microprocessor, a programmable computer or an electronic circuit). In some embodiments, such a device may perform one or more of the most important method steps.

Depending on certain implementation requirements, embodiments of the present invention may be implemented in hardware or software. This implementation may be performed using a non-transitory storage medium (such as a digital storage medium, e.g., a floppy disk, DVD, Blu-ray, CD, ROM, PROM and EPROM, EEPROM or FLASH) on which electronically readable control signals are stored, which cooperate (or can cooperate) with a programmable computer system to perform the corresponding method. Thus, the digital storage medium may be computer readable.

Some embodiments of the invention comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed.

Generally, embodiments of the present invention can be implemented as a computer program product with a program code, when the computer program product runs on a computer, the program code is executable to perform one of the methods. The program code may, for example, be stored on a machine readable carrier.

Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier.

In other words, an exemplary embodiment of the present invention is, therefore, a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.

Therefore, a further embodiment of the invention is a storage medium (or a data carrier or a computer-readable medium) comprising a computer program stored thereon for performing one of the methods described in the invention when the computer program is executed by a processor. The data carrier, the digital storage medium or the recorded medium is generally tangible and/or non-transitory. A further embodiment of the invention is an apparatus as described in the invention, comprising a processor and a storage medium.

Therefore, a further embodiment of the present invention is a data stream or a signal sequence representing the computer program for performing one of the methods described in the present invention. The data stream or the signal sequence can be configured, for example, to be transmitted via a data communication connection, for example via the Internet.

A further embodiment comprises a processing means, for example a computer or a programmable logic device, configured to or adapted to perform one of the methods described herein.

A further embodiment comprises a computer on which is installed the computer program for performing one of the methods described herein.

Another embodiment of the present invention comprises an apparatus or system configured to transmit (for example, electronically or optically) a computer program for performing one of the methods described in the present invention to a receiver. The receiver may be, for example, a computer, a mobile device, a storage device, etc. The apparatus or system may, for example, comprise a file server for transmitting the computer program to the receiver.

In some embodiments, a programmable logic device (e.g., a field programmable gate array) can be used to perform some or all of the functions of the method described in the present invention. In some embodiments, the field programmable gate array can cooperate with a microprocessor to perform one of the methods described in the present invention. Generally, the method is preferably performed by any hardware device.

As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as "/".

Although some aspects have been described in the context of an apparatus, it is clear that these aspects also represent a description of a corresponding method, where a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding apparatus.

Claims (24)

An image reproduction method, comprising: Obtaining first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel image, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image; For the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively acquire more than one single-channel images corresponding to the sample to be observed; superimposing more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed; and Image processing is performed on the second image based on image processing information corresponding to the first image. The method according to claim 1, wherein the first metadata information further includes image processing information respectively corresponding to one or more single-channel images constituting the first image, and before synthesizing more than one single-channel images corresponding to the sample to be observed, the method further includes: Image processing is performed on one or more single-channel images corresponding to the sample to be observed based on image processing information corresponding to one or more single-channel images constituting the first image. The method according to claim 1 or 2, wherein the image processing includes artificial intelligence (AI) analysis, the image processing information includes an AI analysis target and information about an AI analysis tool, and performing image processing based on the image processing information includes: According to the information about the AI analysis tool, acquiring the AI analysis tool; and Using the AI analysis tool, image processing is performed to achieve the AI analysis goal. The method of claim 3, wherein the AI analysis tool comprises one or more AI analysis models; and the information about the AI analysis tool comprises at least one of the following: Used to obtain relevant information of each AI analysis model; Each AI analysis model. The method of claim 4, wherein the information about the AI analysis tool also includes feature information of each AI analysis model. The method of claim 5, wherein the AI analysis tool comprises a plurality of AI analysis models; after acquiring the AI analysis tool according to the information about the AI analysis tool, the method further comprises: Determining a target AI analysis model from the multiple AI analysis models; The use of the AI analysis tool to perform image processing to achieve the AI analysis goal includes: Using the target AI analysis model, image processing is performed to achieve the AI analysis target. The method of claim 6, wherein determining a target AI analysis model from the plurality of AI analysis models comprises: Receive AI analysis requirements input by users; and The target AI analysis model is determined according to the AI analysis requirements and the characteristic information of each AI analysis model. The method of claim 6, wherein determining a target AI analysis model from the plurality of AI analysis models comprises: displaying information about the plurality of AI analysis models; and Based on the user's selection input, a target AI analysis model is determined from the multiple AI analysis models. The method of claim 3, wherein the AI analysis target includes at least one of the following: Determine cell confluence; Determine the cell count; or Determine the cell transfection efficiency. The method according to claim 1, wherein before respectively acquiring more than one single-channel images corresponding to the sample to be observed based on the parameter information respectively corresponding to the more than one single-channel images, the method further comprises: outputting first prompt information, wherein the first prompt information is used to prompt a user to confirm whether to generate the second image for the sample to be observed; and A first instruction is received, wherein the first instruction instructs a user to confirm generating the second image for the sample to be observed. The method of claim 1, wherein, for the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively acquiring the more than one single-channel images corresponding to the sample to be observed comprises: For each single-channel image constituting the first image, adjusting parameter settings of an image generating device based on parameter information corresponding to the single-channel image; and Using the adjusted image generation device, a single-channel image corresponding to the sample to be observed is acquired. The method of claim 11, wherein before adjusting the parameter settings of the image generating device, the method further comprises: determining, based on parameter information corresponding to the single-channel image, whether parameter settings of the image generating device can be adjusted; and The adjusting the parameter settings of the image generating device based on the parameter information corresponding to the single-channel image includes: When the parameter setting of the image generating device can be adjusted, the parameter setting of the image generating device is adjusted based on the parameter information corresponding to the single-channel image. The method of claim 11, wherein before using the adjusted image generation device to obtain a single-channel image corresponding to the sample to be observed, the method further comprises: Determining whether parameter settings for the image generating device are effective; The step of using the adjusted image generation device to obtain a single-channel image corresponding to the sample to be observed includes: After the parameter setting of the image generating device takes effect, the adjusted image generating device is used to acquire a single-channel image corresponding to the sample to be observed. An image reproduction method, comprising: Obtaining first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information includes parameter information respectively corresponding to the more than one single-channel images and image processing information respectively corresponding to one or more single-channel images constituting the first image; For the sample to be observed, based on the parameter information respectively corresponding to the more than one single-channel images, respectively acquire more than one single-channel images corresponding to the sample to be observed; performing image processing on one or more single-channel images corresponding to the sample to be observed based on image processing information corresponding to one or more single-channel images constituting the first image; and More than one single-channel images corresponding to the sample to be observed are superimposed to generate a second image corresponding to the sample to be observed. The method of claim 14, wherein the image processing includes artificial intelligence (AI) analysis, the image processing information includes an AI analysis target and information about an AI analysis tool, and performing image processing based on the image processing information includes: According to the information about the AI analysis tool, acquiring the AI analysis tool; and Using the AI analysis tool, image processing is performed to achieve the AI analysis goal. The method of claim 15, wherein the AI analysis tool comprises one or more AI analysis models; and the information about the AI analysis tool comprises at least one of the following: Used to obtain relevant information of each AI analysis model; Each AI analysis model. The method of claim 16, wherein the information about the AI analysis tool also includes feature information of each AI analysis model. The method of claim 17, wherein the AI analysis tool comprises a plurality of AI analysis models; after acquiring the AI analysis tool according to the information about the AI analysis tool, the method further comprises: Determining a target AI analysis model from the multiple AI analysis models; The use of the AI analysis tool to perform image processing to achieve the AI analysis goal includes: Using the target AI analysis model, image processing is performed to achieve the AI analysis target. The method of claim 18, wherein determining a target AI analysis model from the plurality of AI analysis models comprises: Receive AI analysis requirements input by users; and The target AI analysis model is determined according to the AI analysis requirements and the characteristic information of each AI analysis model. The method of claim 18, wherein determining a target AI analysis model from the plurality of AI analysis models comprises: displaying information about the plurality of AI analysis models; and Based on the user's selection input, a target AI analysis model is determined from the multiple AI analysis models. An image reproduction device, comprising: A first acquisition module is used to obtain first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel image, and the first metadata information includes parameter information corresponding to the more than one single-channel images respectively and image processing information corresponding to the first image; A second acquisition module is used to acquire, for the sample to be observed, more than one single-channel images corresponding to the sample to be observed based on the parameter information respectively corresponding to the more than one single-channel images; a generating module, configured to superimpose more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed; and A processing module is used to perform image processing on the second image based on the image processing information. An image reproduction device, comprising: A first acquisition module is used to obtain first metadata information corresponding to a first image, wherein the first image is composed of more than one single-channel images, and the first metadata information includes parameter information corresponding to the more than one single-channel images and image processing information corresponding to the one or more single-channel images constituting the first image; A second acquisition module is used to acquire, for the sample to be observed, more than one single-channel images corresponding to the sample to be observed based on the parameter information respectively corresponding to the more than one single-channel images; a processing module, configured to perform image processing on one or more single-channel images corresponding to the sample to be observed, based on image processing information corresponding to one or more single-channel images constituting the first image; and A generating module is used to superimpose more than one single-channel images corresponding to the sample to be observed to generate a second image corresponding to the sample to be observed. An image reproduction device, comprising: processor; a memory for storing instructions executable by the processor; Wherein, when the instruction is executed by the processor, the processor is used to execute the method as described in any one of claims 1-13 or any one of claims 14-20. A computer-readable storage medium having instructions stored thereon, when the instructions are executed by a processor, the processor is configured to execute the method according to any one of claims 1 to 13 or any one of claims 14 to 20.